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Real-world robotics systems deal with data from a multitude of modalities,especially for tasks such as navigation and recognition. The performance ofthose systems can drastically degrade when one or more modalities becomeinaccessible, due to factors such as sensors' malfunctions or adverseenvironments. Here, we argue modality hallucination as one effective way toensure consistent modality availability and thereby reduce unfavorableconsequences. While hallucinating data from a modality with richer information,e.g., RGB to depth, has been researched extensively, we investigate the morechallenging low-to-high modality hallucination with interesting use cases inrobotics and autonomous systems. We present a novel hallucination architecturethat aggregates information from multiple fields of view of the localneighborhood to recover the lost information from the extant modality. Theprocess is implemented by capturing a non-linear mapping between the datamodalities and the learned mapping is used to aid the extant modality tomitigate the risk posed to the system in the adverse scenarios which involvemodality loss. We also conduct extensive classification and segmentationexperiments on UWRGBD and NYUD datasets and demonstrate that hallucinationallays the negative effects of the modality loss. Implementation and models:https://github.com/kausic94/Hallucination

Low to High Dimensional Modality Hallucination Using Aggregated Fields of View